The four parameters of data mining
- The first parameter is to fetch the relevant dataset from the database in the form of a relational query. By specifying this primitive, relevant data are retrieved.
- The second parameter is the type of resource/information extracted. This primitive includes generalization, association, classification, characterization, and discrimination rules.
- The third parameter is the hierarchy of datasets or generalization relation or background knowledge as said earlier in the designing of DMQL.
- The final parameter is the proficiency of the data collected which can be represented by a specific threshold value which in turn depends on the type of rules used in data mining.
Data Mining Query Language
Data Mining is a process is in which user data are extracted and processed from a heap of unprocessed raw data. By aggregating these datasets into a summarized format, many problems arising in finance, marketing, and many other fields can be solved. In the modern world with enormous data, Data Mining is one of the growing fields of technology that acts as an application in many industries we depend on in our life. Many developments and researches have been held in this field and many systems are also been disclosed. Since there are numerous processes and functions to be done in Data Mining, a very well developed user interface is needed. Even though there are many well-developed user interfaces for the relational systems, Han, Fu, Wang, et al. proposed the Data Mining Query Language(DMQL) to further build more developmental systems and innovate many kinds of research in this field. Though we can’t consider DMQL as a standard language. It is a derived language that stands as a general query language to perform data mining techniques. DMQL is executed in DB miner systems for collecting data from several layers of databases.